Mobile communications have become ubiquitous in many areas. In addition to the proliferation of subscribers, the capabilities, and hence data demands, of mobile devices (e.g., “smartphones”) have skyrocketed. Higher data demands by increasing numbers of subscribers translates to dramatically increased traffic demands in wireless communication networks, requiring extensive capital investment to both upgrade and expand network equipment. However, network operators' revenue is not growing at the same rate. For example, one study estimates that bandwidth on 3G mobile networks is growing by approximately 400% annually while the associated revenue from data services is only growing approximately 40% per year.
FIG. 1 depicts a projection of network traffic vs. network operators' revenue over time, with the dashed line vertical bar representing the present time. A “revenue gap” already exists, and is projected to dramatically increase. Traditionally, only flat-rate tariffs have been applied for mobile data. This has only recently been changed. For example, different caps on monthly mobile data traffic have been introduced by some operators.
Both network operators and consumers understand that flat-rate tariffs are not sustainable, and advanced tariff systems are necessary for operators to support current and future data traffic. These tariff systems must account for the network resources actually used, such as total traffic or available bandwidth, into the price of the tariff plans. The key question is how these tariff systems should be formed in order to be simple and understandable for subscribers and capable of reflecting resource consumption in the tariffs adequately. Furthermore the tariffs must be monitored and in some cases redesigned periodically since new smartphone apps and hence usage patterns appear every day, changing the ecosystem. Several approaches have been tried, with only limited success. These include traffic shaping; segmentation of subscribers and differentiated tariffs; and individual database queries.
Traffic shaping is the practice of limiting bandwidth under certain circumstances. Certain identified traffic flows are marked as less desirable, and the network resources allocated to deliver that traffic are limited. The shaping can be of various types, such as shaping the traffic of the user (bandwidth limitation) after reaching a certain limit; or shaping the traffic of an intrusive application (e.g., P2P) in the peak usage hours or beyond. Shaping decisions need sophisticated equipment and policy control, and “fairness” is always a key issue which is typically hard to provide.
Traffic shaping is disadvantageous in many situations. For example, by shaping bytes (i.e., restricting bandwidth) of a subscriber who is willing to pay extra fees for the bandwidth, the operator can lose potential revenue. As another example, traffic shaping applied to a specific application (e.g. P2P) may restrict bandwidth to subscribers who otherwise have relatively small total traffic demands, raising a fairness issue. Furthermore, applications that can have harmful traffic patterns can change. Thus, tracking of application behavior is necessary; for example, on-line video traffic is currently a very resource-consuming application, but shaping is not an applicable answer as bandwidth limitation dramatically reduces the user-perceived quality. Finally, when network utilization is low, traffic shaping may become unnecessary regardless of the former behavior detected for the subscriber or of the application type in question.
Another approach to tariff design is to perform segmentation of subscribers, to analyze the main types of behavior, and apply different tariffs to the different segments. This approach attempts to balance network utilization and prices for the specific usage patterns of identified segments of subscribers. In user segmentation, the “footprint” of each subscriber is extracted from the available data in the network (activity profile, locations, mobility, traffic patterns, billing information, socio-demographic data, financial history, and the like) and analyzed to form groups of subscribers with similar profiles. Understanding the groups will then lead to the tariff decisions of the operator.
To be effective, segmentation should meet two main criteria: similar subscribers should be in the same group, and subscribers within different groups should be dissimilar from each other. The difficulty in segmentation approaches to tariff design comes from understanding the formed segments. The operator does not get a simple description of the formed segments but rather a set of subscriber identifiers with all their footprint data. This raises significant privacy issues. Furthermore, it is not at all clear how the subscriber data in a segment leads to a proper tariff. An intermediate step is necessary in which the network operator attempts to discover the reasons (if such a simple set of reasons even exists) for each segment—that is, why are the subscribers in that segment are grouped together? These reasons can be then translated to tariffs tailored to each segment.
Still another approach to innovative tariff design involves network operators building their own databases for subscriber portfolio management, and performing individual database queries on the collected data to support business related decisions. These queries, however, will only slice a small piece of the subscriber base. Without taking special care on some target variable related to cost and revenue issues, these queries will not ensure that the selected subscriber group will have any meaning with respect to subscriber portfolio management. In addition, with repeated queries it is not trivial at all that the subsequent results can give a complete and mutually exclusive description of the whole subscriber set, even if one individual query may yield a meaningful subscriber group.
A problem common to segmentation and database queries—indeed, to any tariff design that is based on analysis of actual subscriber usage patterns—is privacy. Subscribers are sensitive to network operators collecting extensive data about their usage patterns (e.g., websites visited, videos downloaded, apps utilized, and the like). In many jurisdictions, such data collection and analysis may run afoul of privacy laws or regulations. Furthermore, the potential exists that the data may be leaked, sold, or otherwise exploited, exposing operators to liability for, e.g., identity theft. Thus, ideally, innovative tariff schemes based on subscriber footprints should only access anonymized usage records, from which subscriber identities have been removed.
The Background section of this document is provided to place embodiments of the present invention in technological and operational context, to assist those of skill in the art in understanding their scope and utility. Unless explicitly identified as such, no statement herein is admitted to be prior art merely by its inclusion in the Background section.